Web analytics has become increasingly important as a tool for business intelligence, market research, and for improving the effectiveness of a website or application. In today's business world, revenue often depends on millisecond performance of a website or web application. Businesses and application owners are therefore interested in measuring real users' behaviors and experiences to understand how mobile application and web performance impact the revenue generated for their business. In response to this need, commercial products and services have been developed that measure, collect, and analyze performance data obtained directly from web and mobile users. These products and services may also help correlate the RUM data with critical business metrics in order to better understand and optimize web and application usage. By way of example, mPulse™ is a cloud-computing product offered by SOASTA® of Mountain View, Calif. that allows a business to collect and analyze all of a customer's real user data in real-time. The data collected is stored in the cloud, which allows the business customer to access and analyze large amounts of historical data to spot trends and gain business insights.
Web data analytics companies and the products they offer typically rely upon one or more web services that allows users to rent computers (referred to as “instances”) on which to run their own computer applications. Often times, the web service provides block level cloud storage volumes for use with the cloud-computing instances. In a typical architecture, a single machine instance may be connected to receive RUM data from multiple geographic locations, perform read/write services, and database management. A storage area network (SAN) connected to and accessible to the single instance provides large capacity disk array storage for all of the RUM data across multiple business customers. In this cloud-based data storage architecture, performance bottleneck, I/O throughput, and storage capacity problems can arise as the data becomes increasingly larger over time due to business growth, increased website traffic, and the addition of new customers using the analytics service. For example, a customer may want to access and run analytics on their RUM data collected over the past 30 days (which may involve a hundred or more queries accessing millions or even billions of data points). Performance problems inherent in the traditional cloud-based data storage architecture may cause the customer to experience delays lasting several minutes or longer before the system returns the analytics desired by the customer. Furthermore, system performance problems experienced by one customer can also create poor user experience for other customers who use the same system.